Lipids at high latitudes: investigation of sources, environmental controls, and new potential applications of brGDGT-based paleoclimate proxies
Bibliographic record
Abstract
As high latitude regions continue a decades-long trend of warming at roughly twice the rate of the global average, an understanding of their climatic histories becomes increasing important for predicting their future. Organic molecular proxies preserved in lake sediment archives offer one avenue for reconstructing key elements of such past climates, including their temperature, precipitation, and vegetation regimes. In particular, a class of bacterial membrane-spanning lipids called branched glycerol dialkyl glycerol tetraethers (brGDGTs) form the basis for a paleothermometer that can be applied to reconstruct temperatures as far back as the Cretaceous in sedimentary archives across the globe. \n \nDespite these successes, challenges remain that complicate the development and application of brGDGT-based proxies. First, while they correlate best with temperature and pH, other environmental parameters can influence brGDGT distributions, including seasonality, conductivity, and oxygen availability. Second, it is unknown whether these empirical correlations are the result of a direct physiological response of brGDGT-producing organisms to their environment or an indirect effect resulting from variations in bacterial community composition. Finally, an incomplete understanding of where brGDGTs are produced on the landscape and how they contribute to the sedimentary record hinders our ability to interpret proxies in mixed-source archives. \n \nHerein, I present research addressing each of these three challenges with an emphasis on the Eastern Canadian Arctic and Iceland. First, I develop a technique for grouping brGDGTs based on structural characteristics and show that it can be used to deconvolve the effects of temperature and pH/conductivity. I further find a warm-season bias in brGDGT-derived temperatures and develop calibration equations for temperature and conductivity. Next, I compile >2500 samples from a dozen sample types across the globe and find near-universal trends in the relationships between brGDGTs and temperature, pH, and one another. These commonalities support a physiological basis for observed environmental trends. Finally, by measuring brGDGTs in their intact, polar form, I find that lipid sources in lake catchments can be distinguished and suggest novel applications down core. By advancing our understanding of brGDGTs, my results further our ability to reconstruct key climatic variables from sedimentary archives, especially at high latitudes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".